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Value of patient global assessment on evaluating disease activity in patients with axial spondyloarthritis

2017· article· en· W3031187343 on OpenAlexaboutno aff
Xinrong Wang, Shengqian Xu, Hui Xiao, Jing Cai, Ying Wu, Xun Gong, He-xiang Zong

Bibliographic record

VenueChin J Rheumatol · 2017
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAnkylosing spondylitisBASDAIMedicineAxial spondyloarthritisInternal medicineStatistical significancePhysical therapyDiseaseSacroiliitis

Abstract

fetched live from OpenAlex

Objective To explore the value of patient global assessment (PGA) on evaluating disease activity in patients with axial spondyloarthritis (SpA). Methods A total of 222 patients with axial SpA were recruited. Scores of PGA, disease activity index [Bath ankylosing spondylitis disease activity index (BASDAI), ankylosing spondylitis disease activity score (ASDAS)crp] and spondyloarthritis research consortium of Canada (SPARCC) were calculated. Differences of PGA scores between different disease activity groups in axial SpA were compared and correlations between different disease activity index with PGA scores were analyzed. Statistical analyses were performed using Statistical Product and Service Solutions (SPSS) software (version 17.0). Comparison of frequency among different groups was performed by χ2 test. Rank-sum test was used to compare the median of measurement data in different groups when the data were skewed in distribution. Cut-off value of PGA for assessing disease activity in axial SpA was calculated by ROC curve. Results Medians of PGA score in groups with BASDAI remission[3(1,4) vs 5(4,7)] and ASDAScrp remission [1(1,2) vs 4(2,5)] were lower than that in disease activity group (P<0.01). BASDAI scores [1.80(1.20, 2.90) vs 3.40(2.28, 4.63) vs 5.15(4.08, 5.88)] and ASDAScrp scores [2.19(1.34, 2.76) vs 2.86(2.08, 3.54) vs 4.08(2.96, 4.41)] were significant different among PGA groups (≤3, 4-6 and ≥7) (P<0.01). Differences of SPARCC scores [6.00(0, 18.00) vs 7.50(3.75, 18.00) vs 18.50(6.75, 24.50)] were statistically significant among PGA groups (Z=7.427, P=0.037). Erythrocyte sedimentation rate (ESR) [12.00(5.00, 23.00) mm/1 h vs 19.50(7.00, 44.50) mm/1 h vs 18.00(7.75, 54.75) mm/1 h], C-reactive protein (CRP) [7.85(2.37, 22.49) mg/L vs 10.07(3.02, 28.51) mg/L vs 21.28(7.14, 37.74) mg/L] and Bath ankylosing spondylitis functional index (BASFI) [0.70(0.10, 1.30) vs 2.25(0.60, 3.30) vs 2.85(0.83, 6.53)] were also different among PGA groups (P<0.01, separately). Proportion of axial SpA patients in BASDAI disease activity group or ASDAScrp higher disease activity group were different among PGA groups (P<0.01, separately), while represented as positive correlations (P<0.01, separately). Correlation analyses revealed that PGA was positively correlated with ASDAScrp (r=0.694), BASDAI(r=0.616), SPARCC (r=0.271), ESR (r=0.288), CRP(r=0.215), occipital wall distance (r=0.196), finger-floor distance (r=0.385) and negatively correlated with Sschober's test (r=-0.195) (P<0.05). Receiver operator characteristic (ROC) curve analysis found that PGA-BASDAI AUC was 0.813, the cut off value of PGA was 3.5 and PGA-ASDAScrp AUC was 0.860, the cut off value of PGA was 2.5. Conclusion PGA has good correlations with the disease activity indexes in axial SpA patients. It can also reflect the degree of inflammation in iconography. PGA may reflect disease activity especially when the value of PGA is around 3. Key words: Axial Spondyloarthritis; Disease activity; Patient global assessment

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.314
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2017
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